didier rano wrote:
> Wahh so many books to read.
>> But to analysis graph related to time-series, I don't know if I need
> more a statistic approach or pure mathematic approach. Maybe that I
> could use both approaches.
It really depends on what you mean by pure mathematic approach. Purely
mathematic approach to probabilities and statistics are mostly just
that: purely mathematical. Don't get me wrong, maths is great, and
probabilities/statistics are interesting mathematics topics on their
own, but if you want to handle graphs, time series and all that, I don't
think it will help you much.
I second the book by Wasserman, although it does not treat a lot of time
series stuff. But it is concise and precise (it is written with a
relatively practical POV by someone who is definitely familiar with the
theory; in particular, there are a lot of subtle examples and counter
examples which are well explained, contrary to many other books).
Another book. which I have not read entirely yet, but looks related to
what you are looking for, is the book by Gelman et al.:
"Bayesian Data Analysis", by Gelman A., John B. Carlin
<http://www.rch.org.au/cebu/staff.cfm?doc_id=5690>, Hal S. Stern
<http://www.ics.uci.edu/%7Esternh/>, and Donald B. Rubin.
http://www.stat.columbia.edu/~gelman/book/
Not much theory there, but is really oriented toward data analysis as
the title suggests :)
>> Thanks for all your help, and sorry for my poor background in
> mathematics (I need to learn linear algebre too !)
If you do multivariate analysis, you need to be more than familiar with
linear algebra, I think. I don't know any good reference on this, but
some open courseware may be nice (they have some video, too):
http://ocw.mit.edu/OcwWeb/Mathematics/index.htm
cheers,
David